利用RADARSAT SWA SAR和LANDSAT TM的互补信息确定洪水水体范围

从RADARSAT SAR图像中自动提取洪水水体,一直是一个急需解决而又未能得到解决的问题。主要困难是由于阴影与水体严重相混淆。为此,本文研究发现,通过RADARSAT SWA SAR图像和LANDSAT TM图像上的互补信息,利用RADARSAT SWA SAR图像和LANDSAT TM图像上的水体和阴影进行复合处理,可以从RADARSAT SWA SAR图像上准确、半自动提取洪水水体范围。研究结果表明,该方法不仅适用于平地洪水水体范围的半自动提取,而且更适用于包含山区的大范围洪水水体的半自动提取。     

河北沧州黑龙港地区粮食作物生产潜力估算

本文采用屡次分析法,运用1∶10万TM卫星图象对黑龙港类型区(沧州区域为例)农业生产水平类型划分的结果,分析计算了该区各种类型土地(高、中、低产田)的各级生产潜力和粮食作物生产潜力。得出沧州区域提高粮食生产潜力着眼点在高、中产田,主要措施是增加能量投入,合理用水,改良土壤。     

Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image

The spatial resolution of satellite imagery suitable for earth resources studies has improved from 80 m (Landsat-MSS, launched in 1972) to 0.6 m (QuickBird, launched in 2001). The conventional pixel-based methods developed for medium resolution satellite images are not suitable for classification of very high spatial resolution images, because the spectral responses of particular habitat classes are much more variable. On the other hand, in the original Barnsley–Barr kernel-based reclassification algorithm not only the spectral information of a pixel but also the textural information in the vicinity of the pixel is used when the pixel labeling decision is made. The first step of the kernel reclassification algorithm is to perform an initial classification of the original image. In the second step, the adjacency-event matrices are computed for each pixel according to co-occurrence frequencies of the initial classes in the kernel window. The degree of matching between an adjacency-event matrix corresponding to specific pixel and the set of class-specific template matrices produced during training is the criterion for pixel re-labeling. We extend the original kernel-based reclassification algorithm with a decision tree-based reclassification, simultaneously taking into account the class-specific similarity images, which are a side-product of the original algorithm. The advantage of decision tree-extended approach over the original approach seems to be the ability of the former to consider more input information, thus increasing the Kappa classification accuracy for an Ikonos image of our study area from 0.56 to 0.60, using a nomenclature containing 10 habitat classes.     

从TM图像自动提取洪涝灾情的研究

1991年夏我国江淮地区发生特大洪涝灾害,本文报道应用TM图像提取洪涝灾情信息的试验研究结果,发展了一套从TM图像数值分析入手,把TM包含的丰富光谱信息转化为洪涝灾情信息的图像应用处理技术.计算机自动输出的滁河和水阳江两流域洪涝灾情分布图和面积数据经实地验证准确可信.     

An erosion-based land classification system for military installations

The universal soil loss equation (USLE) has been integrated with a geographic information system known as the geographical resources analysis support system (GRASS) to create a land classification system for use by military trainers and land managers to minimize the environmental impacts of military training activities. The USLE provides an estimate of current average annual sheet and rill erosion based upon factors representing climate, soil erodibility, topography, cover, and conservation support practices. The erosion estimate is compared to erosion tolerance values to produce an expression of the current erosion status. An index of inherent site erodibility is also achieved through manipulation of the USLE. Based on published soil surveys, satellite imagery, and ground-truth vegetation transects, data layers are created within GRASS for each of the component factors of the USLE. Appropriate mathematical operations are performed with the data layers, and color-coded maps are produced that represent the erosion status and erodibility index for each 50-m × 50-m area of soil surface. These maps aid military trainers and land managers in scheduling appropriate kinds and intensities of military training activities.     

Remote Sensing and GIS Technologies as Tools to Support Sustainable Management of Areas Devastated by Landslides

IKONOS panchromatic images from a single year were used to characterise the effects of an extreme rainstorm event on six mountain catchment areas in Venezuela. Image registrations were accomplished using topographic base maps at 1 : 5000 scale giving a mapping accuracy between 37 and 40 m. It was used an 8-bit channel for correction, rectification, filteration and tone enhancements.Landsliding was discerned in the affected watersheds using morphometric criteria, including the shape of the slope failure and its position, exposure bedrock in the scar and deposition of debris down-slope. This study is restricted to the impact analysis of the distribution of landslide erosion scars and the depositional processes on the valley floor. Remote sensing data were combined into a geographic information system (GIS) with planimetric data, contour lines, hydrology and vegetation types to evaluate the distribution of the scars and their effects on the highly populated areas located on the alluvial fans. Hillslope mass wasting induced mass movements, logging and increased the mud and silt in floodwaters affecting settlement down-slope.     

GIS支持的卫星图像土地利用现状分类和制图

应用卫星图像提取专题要素，综合利用多元地理信息，是克服多光谱数据在解像力和分辨率上所存在的局限性的有效途径。ＧＩＳ技术则提供多种处理功能将图像与地理信息复合，提高分类精度。分类图像数据与地形基础要素数据复合，通过彩色扫描绘图机直接绘制高质量的土地利用现状专题地图。     

成都市区植被变化的遥感动态监测

利用1987年和2000年的TM影像,对成都市区植被动态变化进行了分析.首先, 利用1:25万数字地形图对2000年的TM影像进行几何校正,并将1987年的TM影像与其配准.其次,利用TM影像的第四和第三波段计算植被指数,根据植被指数提取植被.第三,对成都市区1987～2000年的植被进行了动态变化分析.研究表明,1987～2000年成都市区植被有增有减,但总的趋势是减少:增加部分集中在老城区,是人工绿化的结果;减少部分主要集中在市区西北和西南的面状区域以及因城市道路扩展而占用的线状区域,这是城市化的必然结果.     

利用遥感技术动态监测大面积农田土壤水分研究

为探讨利用遥感技术动态监测大面积农田土壤水分含量 ,排除农田植被和地形的干扰 ,1 997- 1 998年在甘肃省定西县岔口乡建立了监测样区 ,在地面实测 0～ 50 cm大面积农田土壤水分本底资料和收集 5幅 TM卫片影像资料加工处理基础上 ,利用地理信息系统建立了遥感信息 (NDVI和 RVI)与土壤含水量之间的遥感光谱相关模型 ,做出了观测区土壤水分含量分布图 ,得到了初步的大面积农田土壤水分宏观动态监测结果 ,基本实现了利用遥感技术大面积动态监测土壤含水量 ,有效地指导农业春耕生产和快速掌握土壤墒情状况。研究结果表明 ,在波长 60 0～ 1 0 50 nm光谱段 ,土壤含水率与光谱反射率之间存在显著的负相关关系 (α<0 .0 5) ;利用遥感技术建立的 TM光谱水分监测模型 ,其模型监测 0～ 2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测2 0～ 50 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,并且建立了热惯量与土壤含水量之间的相关模型 ,给出了决定系数 (R2 )分析结论。     

OPERATIONAL APPLICATIONS OF SATELLITE SNOW COVER OBSERVATIONS1

ABSTRACT: Several federal and state water resources agencies and NASA have recently completed an Applications Systems Verification and Transfer (ASVT) project on the operational applications of satellite snow cover observations. When satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error from 15 percent to 10 percent on three study basins; and modeling studies on the Boise River basin in Idaho indicated that satellite snow cover could be used to reduce short term forecast error by up to 9.6 percent (5 day forecast). Potential benefits from improved satellite snow cover based predictions across the 11 western states total 10 million dollars for hydropower and 28 million dollars for irrigation annually. The truly operational application of the new technology in the West, however, will only be possible when the turnaround time for all data is reduced to 72 hours, and the water management agencies can be assured of a continuing supply of operational snow cover data from space.